Working alongside CCC staff, the workshop was organized by an inspired team comprising of: Gabrielle Allen (University of Wyoming), Randal Burns (Johns Hopkins University), Sebastian Elbaum (University of Virginia), William Gropp (University of Illinois Urbana-Champaign), Manish Parashar (University of Utah), Nils Aschenbruck (IEEE-CS, Osnabrück University), Terry Benzel (IEEE-CS, University of Southern California), and Rick Kazman (IEEE-CS, University of Hawaii). Their organizing efforts were made possible by the generous support of the National Science Foundation (NSF). 

Fostering Generative Discussion

Through a mix of panels, Q&As, plenary conversation, and small, discussion-heavy breakout sessions, participants had the opportunity to both constantly learn from each other as well as contribute their own expertise to the conversation. With Beyond Code attendees representing a variety of sectors — including academia, industry, government, and NGOs — it led to an enriching experience that brought many perspectives to the table.

Throughout the workshop, participants and speakers touched on a number of timely topics, such as:

• Potential changes in the role of the computer scientist
• Educating future computer scientists to meet this evolving field
• Improving the human-AI relationship
• Making AI output more trustworthy and reliable
• Navigating accountability in the AI landscape

From the most technical conversations about how to actually improve AI output to more meta discussions about its increasing role in our everyday lives, it was clear that scaling AI has more than just technological impacts.

A Roadmap for the Future of AI

These subjects, and more, will be explored fully in a forthcoming CCC-published workshop report. The report is anticipated to be released in 2027, synthesizing the many ideas and explorations of the workshop into a concrete set of recommended computing research and policy directions. 

We also invite all members of the computing community to participate in the AI conversation from wherever you are. CCC recently launched the AI Research Ecosystem discussion forum, designed to assess the biggest challenges facing AI research today. We need your input to gauge which challenges are weighing most heavily on the community so that we can better help to address them. 

Stay tuned to the CCC Blog and the CCC LinkedIn Showcase Page for updates and the Beyond Code workshop report’s release. Stay connected with CCC for the latest insights, publications, and opportunities to engage by subscribing here.

The report presents a roadmap for maximizing the potential of citizen science through the contributions of AI — and vice versa — while also demonstrating the broader applications of this union for challenges across ecological, infrastructural, clinical, and other domains. “We are entering a brave new world where we are renegotiating the relationship between humans and machines,” says Lucy Fortson (University of Minnesota), one of the report’s lead authors. “Investing in human-machine teaming research for citizen science is investing in … accelerating scientific output.”

The report, also co-led by authors Tanya Berger-Wolf (The Ohio State University), Kevin Crowston (Syracuse University), Haley Griffin (Computing Research Association), Corey Jackson (University of Wisconsin-Madison), Saiph Savage (Northeastern University), and Lea Shanley (International Computer Science Institute and GNIES, University of Wisconsin-Madison), is the culmination of extensive visioning. The findings are most notably informed by discussions at the CCC Grand Challenges for the Convergence of Computational and Citizen Science Research workshop on April 8-9, 2025 in Washington, D.C., as well as two virtual roundtables on the topic. In total, 46 experts across computing research, NGOs, philanthropy, industry, and federal agencies came together to envision “how humans and machines may team up to solve some of the world’s most pressing scientific problems,” articulating specific next steps for making that future a reality.

Key Priorities and Opportunities for Growth

This report centers the immense opportunity that arises when human talent is at the core of emerging technologies. Volunteer citizen scientists are capable of data labeling, analysis, and creativity that machines are simply currently incapable of or may not have the resources to perform. Scaling these citizen science efforts would help close the gap between the sheer volume of data that computational technologies are able to produce and the data-based interpretations scientists can then apply to solving complex questions.

Five strategic priorities are identified for increasing this convergence of computational technology and citizen science:

• Create novel ways for humans and machine learning/AI to interact, enabling multi-agent teams to accelerate scientific discovery while balancing productivity, accuracy, engagement, and the education of participants. 
• Craft better, more responsive feedback loops that connect volunteers, scientists, project teams, and other stakeholders in meaningful ways in order to sustain participation and ensure data reliability.
• Establish trustworthy, transparent, and reliable systems that make volunteers feel respected and included.
• Ensure the security and privacy of data, safeguarded against threats in order to strengthen credibility, improve societal uptake of results, and empower more people to contribute to research. 
• Design and implement an infrastructure that can support large-scale participation and both the human and technological needs that underpin it.

Future Recommendations

These strategic priorities guide detailed recommendations for the future research directions and other actions that support the goal of large-scale convergence. They call on researchers, federal agencies, and industry professionals

• Creating a national infrastructure for convergence that establishes sustained platforms, governance systems, and the physical/cyber architecture required to support scalable, trustworthy, and nationwide convergence efforts.
• Focusing on the foundational scientific and socio-technical investigations required to advance convergence, focusing on developing new models, metrics, and frameworks for human-AI interaction, trust, and accountability.
• Developing the necessary human capital, including the skills, knowledge, and organizational structures, to create, manage, and participate in convergence projects across all sectors.

Read the Full Report

The Grand Challenges for the Convergence of Computational and Citizen Science Research report is available now on the CCC website. It provides a detailed roadmap of not only the full benefits of convergence and human-centered computing, but clear, actionable steps for making it possible. 

We encourage all members of the computing community to read the full report here.

Call for Papers: SEMANTiCS 2026 Blue Sky Ideas Track

Event: SEMANTiCS 2026

Track: Blue Sky Ideas

Date: Tuesday, September 15, 2026 – Thursday, September 17, 2026

Location: Ghent, Belgium

Website: https://2026-eu.semantics.cc/

Aims and Scope

The Blue Sky Ideas Track at SEMANTiCS 2026 seeks visionary ideas addressing long-term challenges and opportunities in the semantic web and knowledge graph research field. We invite submissions of original papers that tackle open questions and unexplored ideas, narrowing in on one of the following foci:

• Solution: Present groundbreaking solution concepts or paradigms to long-term, well-known (conceptual or technical) challenges in the field.
• Problem:Clearly describe and specify a major challenge that, once solved, would have a significant impact on the research community, alike to the “millenium problems” in mathematics.
• Application: Describe a relevant application problem from industry to be solved with knowledge graphs and semantic technologies.
• Cross-disciplinary research: Identify emerging and promising cross-disciplinary research ideas; argue for promising paradigms to be learned and adapted from other research disciplines; discuss the potential impact of knowledge graphs on other research disciplines.
• Societal aspects: Present ideas and visions of contributing to solving societal challenges such as climate change, safeguarding democracy in the digital world, etc.

Some Topics of Interest Include (but are not limited to): 

• Neurosymbolic AI
• Knowledge graphs and language models
• Human-AI interaction and knowledge graphs
• Semantics and distributed ledgers
• Knowledge graphs and robotics
• Hybrid intelligence
• Spatio-temporal and multi-modal knowledge graphs
• Graph foundation models
• Knowledge‑driven data quality & governance
• Trust‑weighted and uncertainty‑aware knowledge graphs
• Cognitive knowledge graphs
• On‑device / edge knowledge processing
• Self‑maintaining / self‑healing knowledge graphs
• Causal knowledge graphs
• The impact of AI agents and agentic systems on knowledge graphs
• Quantum computing and knowledge graphs
• Brain-knowledge graph interfaces

Review Criteria

Submissions will be assessed based on their novelty and quality of argumentation. Specific criteria include the level to which submitted papers: 

• Contribute provocative and out of the box propositions
• Propose high-risk/high-gain ideas
• Differ from the mainstream research
• Address a long-term time horizon till the foreseen adoption by the research community

Submission Guidelines

Submissions must be no more than eight pages, excluding references. Please follow the same formatting guidelines as the SEMANTiCS research track. 

Submit via: https://easychair.org/conferences?conf=semantics2026

Important Dates

Submission Deadline: May 30, 2026

Notification of Acceptance: June 30, 2026

Presentation Date: July 16, 2026

Presentation and Awards

The Program Committee will select three winners to present their work at the SEMANTiCS conference. The specific prizes will then be based on public voting amongst these three submissions. With generous support from CCC, the following prizes will be awarded as travel grants:

First Prize: $1,000

Second Prize: $750

Third Prize: $500

These three finalist papers will be published in the main conference proceedings (IOS Press). All other accepted submissions will be published in CEUR.

Blue Sky Track Chairs

Heiko Paulheim, University of Mannheim, DE

Marta Sabou, Vienna University Economics and Business (WU), AT

Contact email: semantics2026-bluesky@easychair.org

The buzz around artificial intelligence (AI) is undeniable, with daily headlines touting its revolutionary potential. But for AI to truly transform science and society, we need to look beyond the impressive demos and massive models and ensure we achieve the desired impacts in a deliberate, responsible, secure way.

Last week at the AAAS 2026 Annual Conference in Phoenix, Arizona, a panel organized by the Computing Community Consortium (CCC) titled “Shaping the Future of AI’s Impact on Society” captivated a crowded room of researchers and media representatives. Manish Parashar (University of Utah) moderated the panel, and the speakers were Rayid Ghani (Carnegie Mellon University), Carla P. Gomes (Cornell University), and Elham Tabassi (Brookings Institution). Haley Griffin (CCC) and Michela Taufer (University of Tennessee, Knoxville) organized the panel.

Redefining Scale

The theme of this year’s AAAS conference was “Science at Scale.” So what about scaling AI? As Ghani eloquently put it, “Scale means, ‘How do I scale the impact of the work?’ It’s not the model, it’s the impact.” He shared compelling examples from his work as part of the Data Science for Social Good program on preventing homelessness and managing mental health crises, where AI is designed to be proactive and supportive rather than reactive and autonomous. He also emphasized the importance of reusability — while a lot of models are very context-specific and need to be customizable, the ubiquity of many social issues means that a model that benefits one community is likely to help in another. The goal isn’t just a bigger, more complex algorithm, but measurable, positive changes in people’s lives. 

The panelists also challenged the traditional definition of scaling by sharing translational approaches, where research in one domain can be applied to another. As Gomes put it, “It’s easier to get funding for studying birds than for addressing social issues; large language models are powerful across domains.” She explained that models trained to track bird migration, for instance, can also be adapted to predict materials properties or to map poverty. This is especially important when research funding for social issues is limited.

Building Trust Through Robust Evaluation

A significant gap currently exists between AI’s advertised capabilities and its real-world performance. Tabassi, a key architect of the National Institute of Standards and Technology’s AI Risk Management Framework, underscored at the panel that improving trust in AI is fundamentally a socio-technical problem that requires socio-technical solutions. It is a slow, context-specific process that requires transparency about both the limits and capabilities of AI systems. “Trust is hard to build… and it doesn’t scale as fast technology scales,” she noted. She pointed out that current benchmarks for AI success often measure narrow, task-specific capabilities rather than reliable, safe, or privacy-enhancing deployments. 

Tabassi suggested a national infrastructure specifically dedicated to AI evaluation, similar to how the National Artificial Intelligence Research Resource (NAIRR) aims to provide computational data. Gomes emphasized that real-world AI systems often need to be designed with multiple objectives in mind, including social, economic, and environmental considerations, which in turn requires multi-objective Pareto optimization. As an example, she highlighted the strategic planning of hydropower expansion in the Amazon basin, where AI can help meet growing energy needs while reducing adverse impacts on both people and nature.

Addressing Resource Disparities and Systemic Challenges

The conversation also tackled practical hurdles. Academia often lacks the data and computational resources to compete with industry, particularly with foundational models, where the user queries, system responses, and infrastructure are proprietary. This creates an imbalance that hinders independent research and evaluation. Parashar also pointed out the lack of effective models and incentives for private-public partnerships. He highlighted the model being explored by the Responsible AI Initiative in Utah which uses innovation, sandboxes, and regulatory mitigation to catalyze partnerships between academic researchers, entrepreneurs, and government.

Beyond technical concerns, the panelists confronted the ethical quandaries of AI infrastructure itself. A poignant question from the audience highlighted how corporate AI data centers can negatively impact marginalized communities through pollution. The consensus was that while new AI-specific regulations are evolving, existing agencies (such as the Environmental Protection Agency [EPA] and Federal Trade Commission [FTC]) need greater resources and mandates to enforce current regulations in an AI-powered world.

The Path Forward

The panel’s insights offer a roadmap for the future of AI research:

• Prioritize Positive Human Impact: Measure AI success by real-world outcomes and impacts, not just technical model-centric metrics.
• Scientist LLMs: Scientific reasoning is lacking from current LLM systems; to solve a problem, they need to truly understand it, which requires thinking like a scientist.
• Full Stack Governance: AI systems need to have end-to-end governance so that if an error occurs, it is clear which aspect is the cause (e.g., chip designer, model developer, data supplier). The governance needs to withstand robust evaluation.
• Invest in Interdisciplinary Research: Foster collaboration between AI experts and domain scientists to build customized, reasoning-based AI. AI researchers must become pseudo-experts and savvy collaborators in the fields they are helping (e.g., materials science, or ecology, or social sciences).
• Strengthen Evaluation: Develop robust, scientifically valid, transparent, and dynamic AI evaluation infrastructures that assess trustworthiness, reliability, and societal impact in deployed settings.
• Center Impacted Humans: Impacted communities must lead the problem-definition process, and be involved throughout the life of a research project.
• Democratize Access: Provide researchers, particularly in academia, with greater access to data and computing resources to encourage broad innovation and evaluation.
• Empower Regulatory Bodies: Equip existing regulatory agencies to enforce regulations and standards in an evolving AI landscape.
• Promote Partnerships: Develop models, mechanisms, and incentives for public-private partnerships that ensure AI innovations have a responsible impact on science and society at scale.

Get Involved

The AAAS panel was an exciting opportunity for panelists to examine the future of AI with input from community members. It is also part of a broader CCC effort to help create a thriving and responsible AI research ecosystem. We invite all members of the computing community to participate in the AI Research Ecosystem (AIRE) discussion forum, where you can share your opinions about the biggest challenges facing AI research today to shape the research pathways of tomorrow.

The workshop will take place from February 25-26, 2026 at The Westin hotel in San Francisco, CA. There, participants will engage in panels, breakout group discussions, and report-writing alongside fellow experts, ultimately crafting a research roadmap identifying critical advancements to make this goal a reality — all while digging into the broader implications these advancements would have for the field.

Creating a Roadmap for AI’s Future

Beyond Code will center around envisioning the very future of AI software development, not just current breakthroughs. “I want every attendee to walk away with a clearer vision for how AI-powered development tools and the organizations that depend on them must transform to move beyond code…” says workshop organizing committee member Sebastian Elbaum, Professor of Computer Science at the University of Virginia School of Engineering & Applied Science.  “At the end of the second day I hope we have sketched the skeleton of that revolution.”

To do so, Beyond Code includes an agenda of topics and activities designed to make the necessary next steps for the field concrete. It asks participants to work together to assess key challenges and goals in three main areas:

• Methodology: Improving the workflows, deployments, auditing, and runtimes of AI
• Tools: Making AI more secure and trustworthy
• Users: Improving accuracy in capturing user intent, balancing human ergonomics with machine capability, and evaluating how the human developer role shifts as systems are increasingly automated

Following a panel of visionary speakers on the first day, these three pillars will guide multiple sessions of creative, collaborative ideation. But Gabrielle Allen, organizing committee member and Professor at the University of Wyoming School of Computing, says the committee especially hopes to “spark a productive kind of excitement — where keynotes, panels, and working sessions move experts from industry, academia, national labs, and agencies beyond discussion.” 

The final sessions of the workshop will thus focus on wrap-up documentation, informing the creation of a Beyond Code Workshop Report published by CCC in mid-2026 containing key recommendations for the research field, federal agencies, and industry. It’s an important moment for the CCC, whose aim is to catalyze computing experts and advance the field by helping realize these community-driven visions of the future.

The CCC and IEEE Computer Society look forward to welcoming members of the computing community to an energizing and inspiring workshop. To stay informed with any updates as well as the eventual release of the Beyond Code Workshop Report, keep an eye on the CCC blog and LinkedIn Spotlight Page.

—

The Beyond Code workshop is co-sponsored by the IEEE Computer Society.

This material is based upon work supported by the U.S. National Science Foundation (NSF) under Award No. 2300842. This award supports the Computing Community Consortium (CCC), a programmatic committee of the Computing Research Association (CRA).

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

To that end, the Computing Community Consortium (CCC) AI Research Ecosystem (AIRE) team, with support from the National Science Foundation, is excited to announce the launch of a new, public discussion forum designed to better understand AI research needs: Pillars of the AI Research Ecosystem. 

Better Understanding the AI Research Ecosystem

This discussion forum features a series of key components (aka “pillars”) of the AI research ecosystem that we invite members of the computing community to share their views on, indicating on a sliding scale which ones are currently experiencing the most pressure. These pillars represent different aspects of the research process, like funding, peer review, and translation.

Some other topics we’re seeking insight on are:

• The academia-industry research and talent pipeline
• The publication environment
• Interdisciplinary research spaces
• Public-private partnerships
• Resource accessibility
• Intellectual property
• The pace of advancement

Not every research barrier may be cleanly encapsulated in one of these areas. Therefore, the AIRE discussion forum allows users to submit their own pillars indicating other areas they believe are facing stress in the current AI research environment.

Once you’ve shared your own responses, you can then see the anonymized assessments of the rest of the community. We hope these polls will represent the broad range of perspectives of stakeholders in the AI research ecosystem across academia, industry, government agencies, and NGOs.

Shaping the Future of AI Research

This discussion forum is a rare chance to make your voice heard and truly shape the world of AI research. The responses will help inform a series of virtual roundtable discussions as well as a multi-day CCC workshop with experts in the field as they hone in on the biggest challenges the AI research community faces. They will then use the perspectives demonstrated on the forum as they put together a workshop report released publicly by CCC, that maps out resources and strategies to help address those challenges. Ultimately, your response helps ensure that trustworthy and effective AI advances can occur, guided by the needs of the community.

CCC is enthusiastic about this opportunity to engage with the community and solicit a broad range of perspectives. We look forward to receiving your contributions soon!

Make Your Voice Heard Here >>>

This material is based upon work supported by the U.S. National Science Foundation (NSF) under Award No. 2300842. This award supports the Computing Community Consortium (CCC), a programmatic committee of the Computing Research Association (CRA).

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Computing research continues to move quickly — and the questions facing the field are only growing in scope, complexity, and consequence. From new directions in AI and data systems to long-term challenges in infrastructure, security, and societal impact, identifying where the field should invest next remains a central task of the Computing Community Consortium (CCC). To support that work, CCC is now accepting nominations for new members of its Council.

Why Serve on the CCC Council?

For those familiar with CCC’s mission, the CCC Council is where much of the organization’s visioning work takes shape. Council members help surface emerging research opportunities, guide community-driven initiatives, and shape activities that inform federal agencies, funders, and partner organizations. Serving on the Council offers a direct opportunity to help steer CCC’s agenda and contribute to research conversations with lasting national impact.

Who Should Consider a Nomination?

CCC encourages self-nominations from researchers with a strong record of research and service who are eager to engage in community leadership. Council members span a wide range of research areas, institutional contexts, and career paths, and bring different perspectives to identifying future directions for computing research. Nominations of colleagues are also welcome.

If you have ideas about where the field should go next — and are interested in working collaboratively to help advance them — CCC strongly encourages you to put your name forward. We also encourage you to share this opportunity with a colleague you believe would be a strong fit and may be interested in serving.

Nomination Form >>>

What Is the Commitment?

Council members serve three-year terms and participate actively in CCC’s work. This includes leading or contributing to visioning activities and task forces, engaging with government agencies and partner organizations, and contributing to CCC workshops and publications. Members take part in monthly virtual meetings and attend three in-person Council meetings each year.

Nominations Deadline

Nominations are open through February 13, 2026, for Council terms running from July 1, 2026, through June 30, 2029. Additional details on Council responsibilities and the nomination process are available on the CCC website.

CCC’s impact depends on engaged researchers willing to help shape the future of the field. If you know a colleague whose perspective and experience would strengthen the Council’s work, please consider sharing this nomination opportunity with them. If that describes you as well, we hope you’ll submit a nomination.

Nomination Form >>>

The Computing Community Consortium (CCC), a programmatic committee of the Computing Research Association (CRA), is supported by the U.S. National Science Foundation (NSF) under Award Nos. 2300842. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

We are excited to announce the winners of the 2025 IEEE International Conference on Data Mining (ICDM) Blue Sky Ideas Track, sponsored by the Computing Community Consortium (CCC)! ICDM 2025 was held in Washington, D.C. on November 12-15, 2025. Now in its 24th year, ICDM is a premier international conference for advancing data mining research, and covers all aspects of data mining, including algorithms, software, systems, and applications.

ICDM 2025 Blue Sky Winners

First Place

Truth Without Comprehension: A BlueSky Agenda for Steering the Fourth Mathematical Crisis

Runlong Yu, University of Pittsburgh

Xiaowei Jia, University of Pittsburgh

Second Place

Navigating Between Explainability and Extractability in Machine Learning as a Service

Ojas Nimase, University of Southern California

Yue Zhao, University of Southern California

Yushun Dong, Florida State University

Third Place

Toward Interpretable and Reliable Time Series Reasoning: A BlueSky Vision

Kanghui Ning, University of Connecticut

Zijie Pan, University of Connecticut

Yushan Jiang, University of Connecticut

Anderson Schneider, Morgan Stanley

Yuriy Nevmyvaka, Morgan Stanley

Dongjin Song, University of Connecticut

Please join us in congratulating the IDCM 2025 winners! 

CCC sponsors many Blue Sky tracks at various conferences in computing every year. If you are interested in requesting sponsorship of a Blue Sky track at your conference, please email blueskyideas@cra.org. Requests need only include a brief description of the conference and a proposed list of program committee members for the track. For more information — including guidelines for conference program committees, recommendations for selecting winners, and logistics for issuing CCC-sponsored travel awards to the winners, as well as a sample call for papers for a Blue Sky Ideas track — visit our website.

Stay connected with CCC for the latest insights, publications, and opportunities to engage by subscribing here.

Despite computing’s central role in modern science, industry, and society, the field has not yet defined computing-specific grand challenges that can focus research, articulate shared ambitions, and catalyze progress at scale. A new CCC white paper, The Imperative for Grand Challenges in Computing, argues that the maturity and influence of computing now demand a deliberate effort to identify such challenges — and that doing so will shape the next generation of discoveries in our field.

Computing has repeatedly transformed how the world learns, works, communicates, and innovates. Yet while other disciplines have long used grand challenges to unify research communities around ambitious and measurable goals, computing’s most significant advances have often emerged without a field-wide framework or intentional long-term strategy. The authors contend that this is a missed opportunity: computing should not only support other disciplines’ grand challenges; it should articulate grand challenges of its own.

This paper — authored by William Regli, Rajmohan Rajaraman, Daniel Lopresti, David Jensen, Mary Lou Maher, Manish Parashar, Mona Singh, and Holly Yanco — makes the case that now is the time to define such challenges. Doing so will help the field take a long view, guide translational impact, and build the research structures necessary to address the profound transformations underway as society increasingly relies on computational systems.

Why Grand Challenges — and Why Now?

The white paper argues that computing is in the midst of a major societal transition: our systems, institutions, and scientific practices increasingly run on computational substrates. Despite this, computer science has not defined grand-scale research questions commensurate with the field’s growing responsibility and potential to drive transformational change.

Grand challenges offer a mechanism to:

• Focus the community’s attention on ambitious, high-impact research goals
• Align computing innovations with pressing societal needs
• Provide a shared framework for long-term progress
• Clarify desired outcomes and impacts
• Engage interdisciplinary partners around computational advances

The authors note that the pace of progress in computing can obscure the need for intentional direction. Breakthroughs often appear sudden, but many emerged from decades of foundational, use-inspired, and translational work that only later reached critical tipping points. A long-term, challenge-driven mindset can help the field recognize and shape those trajectories earlier.

Learning from History — Inside and Outside of Computing

To contextualize the need for computing-specific grand challenges, the paper surveys examples from science and engineering: Hilbert’s Problems, the Longitude Act, the Human Genome Project, the Apollo Program, and several federal “moonshots.” These efforts illustrate how grand challenges can mobilize researchers, drive infrastructure development, and create clarity around ambitious but measurable goals.

The paper also reviews historical examples within computing:

• DARPA’s Strategic Computing Initiative
• DARPA Grand Challenges in autonomous systems
• CRA’s 2002 and 2003 grand challenges conferences
• The recent 20-year retrospective examining why many early challenges remain unresolved

These experiences highlight both successes and challenges, including the need for specificity, measurable progress indicators, and a deep understanding of application domains. They also illustrate how computing revolutions often result from contributions across many organizations and subfields—not from isolated breakthroughs.

Toward a Framework for Grand Challenges in Computing

To help the community define future challenges, the authors propose five criteria that a compelling grand challenge should meet:

• Impact: Address a critical need or urgent societal problem
• Ambition: Push beyond current computing capabilities
• Feasibility: Be just barely achievable with emerging ideas
• Interdisciplinarity: Draw on expertise across computing and other fields
• Measurability: Allow progress to be meaningfully assessed

The framework is designed to support grand challenges that reflect computing’s unique scale, complexity, and role in society.

Drawing on CCC roundtables, the CRA Summit, and discussions with early-career researchers, the paper identifies several broad thematic vectors that recur across the community’s thinking, including:

• Advances in computational abstraction
• New models of computation (e.g., qubits, photons, neurons, chemicals)
• Improved modeling and simulation capabilities
• Sense-and-act systems and robotics
• The role of AI in design, discovery, and cognition
• Human–machine symbiosis
• Trust, transparency, and security in computational systems

These vectors are not grand challenges themselves. Instead, they represent promising directions from which specific, audacious challenge problems may emerge.

A Call to Action

The authors emphasize that identifying the next grand challenges in computing must be a community-wide effort. Grand challenges are most powerful when they reflect a wide range of perspectives, expertise, and motivations across the research ecosystem.

The CCC encourages computing researchers to help advance this process by:

• Participating in CCC discussions on LinkedIn
• Proposing a CCC Blue Sky Ideas Track for a leading conference
• Submitting a CCC Visioning Workshop proposal
• Nominate yourself or a colleague to join the CCC Council and contribute directly to long-term community visioning
• Engaging colleagues, students, and collaborators in conversations about what the grand challenges of computing should be — and sharing those discussions with CCC

Grand challenges have shaped eras of scientific progress. As computing continues to transform both science and society, the field now has an opportunity — and a responsibility — to define the ambitious questions that will drive the next decade of innovation.

To dive deeper into the ideas and motivation behind this community-wide effort, we invite you to read the full white paper, The Imperative for Grand Challenges in Computing.

Product teams and trust and safety practitioners face an increasingly complex challenge: how to build technologies that reduce unintended harm, anticipate misuse, and protect users who interact with systems from positions of greater digital vulnerability. While industry teams continue to invest in safety engineering and risk mitigation, deeper engagement with emerging research can strengthen these efforts.

A recent Computing Community Consortium (CCC) visioning workshop, summarized in the report Supporting At-Risk Users Through Responsible Computing, brought together experts who examine technology-facilitated harm from multiple angles — computing, human behavior, cybersecurity, and sociotechnical systems. Although the original study was directed at researchers, it highlights several insights directly relevant to technology builders designing and deploying real-world systems.

Below are key lessons from this visioning effort for product, engineering, UX, and trust & safety teams seeking to build safer and more resilient technologies.

Understanding High-Risk User Contexts Improves Safety Design

A foundational takeaway from the CCC study is that users experiencing technology-facilitated harm often interact with systems differently than typical users. Product teams that rely primarily on aggregated analytics or generalized personas may inadvertently overlook scenarios where design assumptions break down.

Researchers highlighted several recurring issues:

• Convenience features can unintentionally expose sensitive information.
  
• Automated detection and moderation may miss the contextual nuances of high-risk scenarios.
  
• Default settings may empower malicious third parties in ways designers did not anticipate.
  

Incorporating targeted user research, misuse case analysis, and risk-scenario mapping early in the design process can help surface these issues before they reach production.

Knowing When Not to Intervene Is Part of Responsible Design

The CCC study underscored a challenge familiar to many trust & safety teams: interventions designed to protect users can sometimes intensify harm instead.

For example:

• Notifications may alert an adversary monitoring a shared device.
  
• Account recovery flows can unintentionally reveal sensitive information.
  
• Automated messages may increase risk in situations with interpersonal conflict.
  

The study emphasizes the importance of:

• Evaluating intervention risks at multiple levels (individual, technical, interpersonal, organizational).
  
• Designing calibrated responses rather than universal solutions.
  
• Consulting experts familiar with high-risk digital environments during feature planning.
  

These insights align with emerging industry practices around harm reviews, red-team exercises, and pre-launch safety assessments.

Research Frameworks Can Strengthen Product Decision-Making

The CCC visioning study compiled tools and frameworks that can support industry efforts to identify and mitigate harm, including:

• Implementation science frameworks (e.g., CFIR, RE-AIM) that help teams account for environmental and organizational factors.
  
• Taxonomies of online harm that can improve detection prioritization and severity scoring.
  
• The User States Framework, developed within industry and recently published, which describes how user context and capability shape digital risk.
  

Product teams can use these tools to:

• Establish shared vocabulary across engineering, UX, trust & safety, and policy.
  
• Evaluate features through a structured lens.
  
• Identify where new controls, visibility settings, or mitigations may be needed.

Advisory Structures Enhance Responsible Development

The study introduces the idea of interdisciplinary advisory models—structures that review designs and technologies before deployment. While framed for research, the underlying concept maps cleanly to industry practice.

In product settings, this could translate to:

• External advisory consultations with experts who work directly on technology-facilitated harm.
  
• Internal cross-functional review boards that combine privacy, security, engineering, UX, and trust & safety perspectives.
  
• Formalized processes to assess unintended consequences before features ship.
  

Many companies maintain ethics or privacy review mechanisms; the study suggests that adding risk specialists can fill important gaps.

Researcher Well-Being Lessons Also Apply to Industry Teams

The CCC study notes the psychological and professional risks faced by researchers who engage with harmful content or sensitive populations. Similar challenges exist for many industry teams, particularly those reviewing reports involving harassment, exploitation, or manipulation.

Key implications for builders include:

• Providing structured support systems for employees who handle high-risk content.
  
• Offering rotation options, workload balancing, and mental health resources.
  
• Building clear guidance and protective protocols into workflows.
  

These supports contribute directly to team resilience and long-term operational stability.

Why These Lessons Matter for Technology Builders

Digital systems shape how people interact, communicate, and keep themselves safe. As technologies evolve—from generative AI to immersive environments and ubiquitous sensing—the potential for misuse evolves with them.

The CCC visioning study provides a research-grounded roadmap for anticipating harm and engineering systems that account for a broader set of real-world contexts. For product teams committed to responsible innovation, these insights offer practical direction for strengthening safety by design.

Download the Report

Technology builders interested in deeper research-driven guidance can explore the full study and supporting materials on the CCC website:

Download Supporting At-Risk Users Through Responsible Computing